Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity

Osmalina N. Rahma; Sastra Kusuma Wijaya; Prawito Prawito; Cholid Badri

doi:10.1109/ISSIMM.2017.8124287

Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity

Osmalina N. Rahma
, Sastra Kusuma Wijaya
, Prawito
, Cholid Badri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72%. These results were useful for classifying AIS based on EEG signals.

Original language	English
Title of host publication	Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology
Subtitle of host publication	Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	180-186
Number of pages	7
ISBN (Electronic)	9781538607459
DOIs	https://doi.org/10.1109/ISSIMM.2017.8124287
Publication status	Published - 29 Nov 2017
Event	2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017 - Surabaya, Indonesia Duration: 25 Aug 2017 → 26 Aug 2017

Publication series

Name	Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
Volume	2017-January

Conference

Conference	2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017
Country/Territory	Indonesia
City	Surabaya
Period	25/08/17 → 26/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Acute ischemic stroke (AIS)
Electroenchepalogram (EEG)
Extreme learning machine (ELM)

Access to Document

10.1109/ISSIMM.2017.8124287

Cite this

Rahma, O. N., Wijaya, S. K., Prawito, & Badri, C. (2017). Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. In Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017 (pp. 180-186). (Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSIMM.2017.8124287

Rahma, Osmalina N. ; Wijaya, Sastra Kusuma ; Prawito et al. / Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 180-186 (Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017).

@inproceedings{6da70de4e2ca4799966daf9026655b8d,

title = "Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity",

abstract = "Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72\%. These results were useful for classifying AIS based on EEG signals.",

keywords = "Acute ischemic stroke (AIS), Electroenchepalogram (EEG), Extreme learning machine (ELM)",

author = "Rahma, \{Osmalina N.\} and Wijaya, \{Sastra Kusuma\} and Prawito and Cholid Badri",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017 ; Conference date: 25-08-2017 Through 26-08-2017",

year = "2017",

month = nov,

day = "29",

doi = "10.1109/ISSIMM.2017.8124287",

language = "English",

series = "Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology",

address = "United States",

}

Rahma, ON, Wijaya, SK, Prawito & Badri, C 2017, Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. in Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017. Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 180-186, 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017, Surabaya, Indonesia, 25/08/17. https://doi.org/10.1109/ISSIMM.2017.8124287

Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. / Rahma, Osmalina N.; Wijaya, Sastra Kusuma; Prawito et al.
Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 180-186 (Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity

AU - Rahma, Osmalina N.

AU - Wijaya, Sastra Kusuma

AU - Prawito, null

AU - Badri, Cholid

PY - 2017/11/29

Y1 - 2017/11/29

N2 - Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72%. These results were useful for classifying AIS based on EEG signals.

AB - Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72%. These results were useful for classifying AIS based on EEG signals.

KW - Acute ischemic stroke (AIS)

KW - Electroenchepalogram (EEG)

KW - Extreme learning machine (ELM)

UR - https://www.scopus.com/pages/publications/85043448686

U2 - 10.1109/ISSIMM.2017.8124287

DO - 10.1109/ISSIMM.2017.8124287

M3 - Conference contribution

AN - SCOPUS:85043448686

T3 - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017

SP - 180

EP - 186

BT - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017

Y2 - 25 August 2017 through 26 August 2017

ER -

Rahma ON, Wijaya SK, Prawito, Badri C. Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. In Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 180-186. (Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017). doi: 10.1109/ISSIMM.2017.8124287

Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity

Abstract

Publication series

Conference

UN SDGs

Keywords

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